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| Registros recuperados : 139 | |
| Registros recuperados : 139 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café; Embrapa Unidades Centrais. |
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Data corrente: |
15/05/2017 |
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Data da última atualização: |
15/12/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
SILVA, G. N.; NASCIMENTO, M.; SANT'ANNA, I. de C.; CRUZ, C. D.; CAIXETA, E. T.; CARNEIRO, P. C. S.; ROSADO, R. D. S.; PESTANA, K. N.; ALMEIDA, D. P. de; OLIVEIRA, M. da S. |
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Afiliação: |
GABI NUNES SILVA, UFV-DE; MOYSÉS NASCIMENTO, UFV-DE; ISABELA DE CASTRO SANT'ANNA, UFV-DBG; COSME DAMIÃO CRUZ, UFV-DBG; EVELINE TEIXEIRA CAIXETA, SAPC; PEDRO CRESCENCIO SOUZA CARNEIRO, UFV-DBG; RENATO DOMICIANO SILVA ROSADO, UFV-DBG; KÁTIA NOGUEIRA PESTANA, CNPMF; DÊNIA PIRES DE ALMEIDA, UFV-IBAA; MARCIANE DA SILVA OLIVEIRA, UFV-DBG. |
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Título: |
Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n. 3, p. 186-193, mar. 2017. |
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Idioma: |
Inglês |
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Notas: |
Título em português: Redes neurais artificiais comparadas com modelos lineares generalizados sob o enfoque bayesiano para predição de resistência à ferrugem em café arábica. |
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Conteúdo: |
The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee. |
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Palavras-Chave: |
Inteligência artificial; Predição. |
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Thesagro: |
Coffea Arábica; Hemileia Vastatrix; Marcador molecular. |
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Thesaurus NAL: |
Artificial intelligence; Genetic markers; Prediction. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/159883/1/Artificial-neural-networks.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/159878/1/Artificial-neural-networks.pdf
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Marc: |
LEADER 02409naa a2200337 a 4500
001 2069618
005 2017-12-15
008 2017 bl uuuu u00u1 u #d
100 1 $aSILVA, G. N.
245 $aArtificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee.$h[electronic resource]
260 $c2017
500 $aTítulo em português: Redes neurais artificiais comparadas com modelos lineares generalizados sob o enfoque bayesiano para predição de resistência à ferrugem em café arábica.
520 $aThe objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.
650 $aArtificial intelligence
650 $aGenetic markers
650 $aPrediction
650 $aCoffea Arábica
650 $aHemileia Vastatrix
650 $aMarcador molecular
653 $aInteligência artificial
653 $aPredição
700 1 $aNASCIMENTO, M.
700 1 $aSANT'ANNA, I. de C.
700 1 $aCRUZ, C. D.
700 1 $aCAIXETA, E. T.
700 1 $aCARNEIRO, P. C. S.
700 1 $aROSADO, R. D. S.
700 1 $aPESTANA, K. N.
700 1 $aALMEIDA, D. P. de
700 1 $aOLIVEIRA, M. da S.
773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 52, n. 3, p. 186-193, mar. 2017.
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Registro original: |
Embrapa Café (CNPCa) |
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